Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state

• Published in: Climate Dynamics
• Main Authors: Volpi, Danila, Guemas, Virginie, Doblas-Reyes, Francisco J.
• Other Authors: Barcelona Supercomputing Center
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Verlag 2017
• Subjects: Àrees temàtiques de la UPC::Energies; Climate science; Seasonal prediction (Meteorology); Decadal climate prediction; Full field initialisation; Anomaliy initialisation; Global coupled model; Previsió del temps; Arctic; Española; Sea ice
• Online Access: http://hdl.handle.net/2117/114139; https://doi.org/10.1007/s00382-016-3373-3

Decadal prediction exploits sources of predictability from both the internal variability through the initialisation of the climate model from observational estimates, and the external radiative forcings. When a model is initialised with the observed state at the initial time step (Full Field Initialisation—FFI), the forecast run drifts towards the biased model climate. Distinguishing between the climate signal to be predicted and the model drift is a challenging task, because the application of a-posteriori bias correction has the risk of removing part of the variability signal. The anomaly initialisation (AI) technique aims at addressing the drift issue by answering the following question: if the model is allowed to start close to its own attractor (i.e. its biased world), but the phase of the simulated variability is constrained toward the contemporaneous observed one at the initialisation time, does the prediction skill improve? The relative merits of the FFI and AI techniques applied respectively to the ocean component and the ocean and sea ice components simultaneously in the EC-Earth global coupled model are assessed. For both strategies the initialised hindcasts show better skill than historical simulations for the ocean heat content and AMOC along the first two forecast years, for sea ice and PDO along the first forecast year, while for AMO the improvements are statistically significant for the first two forecast years. The AI in the ocean and sea ice components significantly improves the skill of the Arctic sea surface temperature over the FFI. The authors acknowledge funding support for this study from the SPECS (ENV-2012-308378) project funded by the Seventh Framework Programme (FP7) of the European Commission and the PICA-ICE (CGL2012-31987) project funded by the Ministry of Economy and Competitiveness of Spain. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación through the Barcelona Supercomputing Center. Peer Reviewed Postprint (author's final draft)